Recently, Sean Byrnes spoke at the Marketing Analytics and Data Science (MADS) Conference in San Francisco. His session titled “Self-service Data Science — Myth or New Reality?,” flanked by an agenda of influential analytics peers, focused on the automation of data analytics. As the Co-Founder and CEO of Outlier he is well versed in how AI can help uncover hidden insights in data with self-service data science. If you were not in town and unable to attend, here’s your chance to get our take on what popped out as the highlights from his talk:

Outlier speaks at the Marketing Analytics Data Science Conference

HIGHLIGHT ONE:

There is a huge shortage of data scientists, which was not a surprise to this audience. According to Inside Big Data, only 40% of the data scientists roles can be filled. How are companies attempting to bridge this hiring gap? Many companies are trying to implement some amount of self-service data science. This might be better reporting or more on-demand dashboards. This solves the challenge for basic questions you know to ask about.

What do you do about the non-basic questions that you aren’t aware of or that you need to ask? For customers, who are concerned about addressing business opportunities or challenges they haven’t considered looking into, they are turning to Automated Business Analysis to provide fast analysis of their mountains of data. And, Gartner states that by 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics.1

HIGHLIGHT TWO:

Currently, most data analysts spend the majority of their time fixing data versus analyzing it. In a Gartner for Marketing Leaders survey, marketers who had dedicated data science resources reported they were more likely to be performing tasks such as data visualization (48%) and data preparation (46%) rather than advanced activities such as modeling and machine learning.2 There is a way to get more out of the data scientists or analysts that are hired, but we have to change how we leverage these resources. What if data analysis could be automated and there was no or very little data preparation required?

HIGHLIGHT THREE:

Not a surprise, but attendees are still inundated with data. Some are looking to automate more process in the collection or analysis phases. Some are trying to spend less time performing data preparation tasks. And, some are attempting to run more experiments to find the best campaign or outcome possible. Sean shared an example of an Outlier client who accidentally ran an experiment and the results were found in their mountain of data.

In short, there are several ways to create a more self-service approach to data science. You should consider what approaches would work best for your company before you get started on this path.

You can identify meaningful trends in your data with Outlier, which is powered by artificial intelligence (AI) to find unexpected changes in your data. Without Outlier, these kinds of insights would require hours, days, weeks or months of analysis. Sign up for a custom demo to hear how customers find the impossible with Outlier.